1. Field of the invention
The present invention relates to personalization of content. More particularly, the present invention relates to user interface techniques and active and passive personalization techniques to enhance a user's personalization experience.
2. Background
With more and more content being continually added to the world wide information infrastructure, the volume of information accessible via the Internet, can easily overwhelm someone wishing to locate items of interest. Although such a large source pool of information is desirable, only a small amount is usually relevant to a given person. Personalization techniques are developing to provide intelligent filtering systems to ‘understand’ a user's need for specific types of information.
Personalization typically requires some aspect of user modeling. Ideally, a perfect computer model of a user's brain would determine the user's preferences exactly and track them as the user's tastes, context, or location change. Such a model would allow a personal newspaper, for example, to contain only articles in which the user has interest, and no article in which the user is not interested. The perfect model would also display advertisements with 100% user activity rates (i.e., a viewer would peruse and/or click-through every ad displayed) and would display only products that a user would buy. Therefore, personalization requires modeling the user's mind with as many of the attendant subtleties as possible. Unfortunately, user modeling to date (such as information filtering agents) has been relatively unsophisticated.
However, personalization content as well as profiles can be difficult for users to digest, especially where such content is dispersed through a web page that often requires a large amount of scrolling. Furthermore, developing a personalization profile can be cumbersome and time consuming. Fill-in profiles represent the simplest form of user modeling for personalization technology. A fill-in profile may ask for user demographic information such as income, education, children, zip code, sex and age. The form may further ask for interest information such as sports, hobbies, entertainment, fashion, technology or news about a particular region, personality, or institution. The fill-in profile type of user model misses much of the richness desired in user modeling because user interests typically do not fall into neat categories.
Feature-based recommendation is a form of user modeling that considers multiple aspects of a product. For example, a person may like movies that have the features of action-adventure, rated R (but not G), and have a good critic review of B+ or higher (or 3 stars or higher). Such a multiple-feature classifier such as a neural network can capture the complexity of user preferences if the interest is rich enough. Text-based recommendation is a rich form of feature-based recommendation. Text-based documents can be characterized using, for example, vector-space methods. Thus, documents containing the same frequencies of words can be grouped together or clustered. Presumably, if a user selects one document in a particular cluster, the user is likely to want to read other documents in that same cluster.
However, it would be advantageous to provide a user with a personalization experience that generates positive perceptions and responses that encourage users to want to use the personalization service, while avoiding those negative perceptions that would discourage users from using the system, in an unintrusive manner so that the user can view content in a manner with which they are already familiar. Positive perceptions from the point of view of a user include, easily developing a profile, easily viewing third party profiles, and easily viewing potentially interesting content.